Answer: positive
Explanation:
The real gross domestic product refers to the value of the output in an economy which has been adjusted for price changes.
There's a positive relationship between the real GDP and tax revenues. This can be used to explain deficit spending during a recession. When there's recession, there'll be a reduction in the output and consumption in the economy. At this point, there'll be a reduction in GDP.
Answer:
-21%
Explanation:
Initial share price = $50
Share price after 1 year = $46
net return = (200 x $46) - $10,000 - ($5,000 x 5%) = $9,200 - $10,000 - $250 = -$1,050
rate of return of margined position = -$1,050 / $5,000 = -0.21 = -21%
when you operate on the margin, your earnings can increase or decrease dramatically. In this case, an 8% price decrease resulted in a 215 lose.
Answer:
$0.79
Explanation:
The Bakery bakes 660 loaves of bread
The cost of baking one bread= $0.46
The total cost of baking all loaves of bread
= $0.46 x 660
=$303.60
The desired mark up is 55% of cost
=55% of $303.60
=55/100 x $303.60
=0.55 x $303.60
= $166.98
Desired revenues = $166.98 +$303.60
=$470.58
The number of sellable breads= 660 - (10% of 660)
=660-66
=594
Desired income is $470.58; sellable output is 594.
price per bread should be
=$470.58/594
=$0.79222
Price per bread = $0.79
In addition to prototyping, Powder Bed Fusion (PBF) AM processes have lately been more widely used to manufacture end-use parts. These changes lead to necessity of higher requirements to quality of a final product. Optimization of process parameters is one of the ways to achieve desired quality of a part.
In addition to prototyping, Powder Bed Fusion (PBF) AM processes have lately been more widely used to manufacture end-use parts. These changes lead to necessity of higher requirements to quality of a final product.
Optimization of process parameters is one of the ways to achieve desired quality of a part. Finite Element Method (FEM) and machine learning techniques are applied to evaluate and optimize AM process parameters. While FEM requires specific information, Powder Bed Fusion Machine Learning is based on big amounts of data. This paper provides a conceptual framework on combination of mathematical modelling and Machine Learning to avoid these issues.
Learn more about Powder Bed Fusion here
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